library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)
Processing
## load global vars:
source("_src/global_vars.R")
# meta_tbl
# clrs
# markers_v6
# markers_v6_super
# cell_type_super_lookup
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
## load data --------------------------------------
## load cohort embeddings
seu_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs/cells.tsv") %>%
mutate(cell_type = ifelse(cell_type == "Monocyte", "Myeloid.cell", cell_type)) %>%
mutate(cell_type = ifelse(cell_type == "Ovarian.cancer.cell", "Ov.cancer.cell", cell_type))
seu_tbl <- read_tsv("/work/shah/isabl_data_lake/analyses/68/74/6874/cells.tsv") %>%
mutate(cell_type = ifelse(cell_type == "Monocyte", "Myeloid.cell", cell_type)) %>%
mutate(cell_type = ifelse(cell_type == "Ovarian.cancer.cell", "Ov.cancer.cell", cell_type))
## load consensus data
consOV_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/consensusOV/SPECTRUM_freeze_v6_consensusOV.tsv") %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
select(cell_id, consensusOV)
## join data
seu_tbl_full <- seu_tbl %>%
left_join(meta_tbl, by = "sample") %>%
filter(therapy == "pre-Rx", cell_type != "Other", tumor_supersite != "Unknown") %>%
rename(UMAP_1 = umap50_1, UMAP_2 = umap50_2) %>%
mutate(cell_type_super = cell_type_super_lookup[cell_type]) %>%
mutate(cell_type = ordered(str_replace_all(cell_type, "\\.", " "),
levels = names(clrs$cell_type))) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal",
"CD45-", sort_short))) %>%
left_join(consOV_tbl, by = "cell_id")
# seu_tbl_full <- seu_tbl_full %>%
# sample_n(10000)
UMAPs
dpi <- 72
base_umap <- ggplot(seu_tbl_full) +
coord_fixed() +
NoAxes() +
theme(legend.position = c(0, 1),
legend.justification = c("left", "top"),
legend.box.just = "left",
legend.margin = ggplot2::margin(1, 1, 1, 1),
#panel.border = element_rect(linetype = 1, color = "black", size = 1),
legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 22))
pt.size <- 0.1
pt.size.mini <- 0.01
pt.alpha <- 0.05
pt.alpha.mini <- 0.02
# median_tbl <- seu_tbl_full %>%
# filter(cell_type != "Other") %>%
# group_by(cell_type, reduction) %>%
# summarise(UMAP_1 = median(UMAP_1),
# UMAP_2 = median(UMAP_2))
ncol1_legend <- list(
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")),
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 9.5, 0), color = "white"),
legend.spacing.x = unit(0, "npc"))
)
umap_site <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = tumor_supersite), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi) +
scale_color_manual(values = clrs$tumor_supersite) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 2,
label.position = "right"))
umap_site_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = tumor_supersite), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi) +
scale_color_manual(values = clrs$tumor_supersite) +
guides(color = F)
umap_site_legend_ncol1 <- cowplot::get_legend(umap_site + ncol1_legend)
umap_site_legend <- cowplot::get_legend(umap_site)
umap_cell_type <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = fct_rev(cell_type)), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
scale_color_manual(values = clrs$cell_type) +
ncol1_legend
umap_cell_type_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = cell_type), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi) +
scale_color_manual(values = clrs$cell_type) +
guides(color = F)
umap_cell_type_legend <- cowplot::get_legend(umap_cell_type)
umap_patient <- base_umap +
geom_point(aes(UMAP_1, UMAP_2, color = patient_id_short), size = pt.size, alpha = pt.alpha) +
scale_color_manual(values = clrs$patient_id_short) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
nrow = 16,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"))
umap_patient_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = patient_id_short), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi) +
scale_color_manual(values = clrs$patient_id_short) +
guides(color = F)
# umap_patient_legend <- cowplot::get_legend(umap_patient)
umap_consOV <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensusOV), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
scale_color_manual(values = clrs$consensusOV, labels = c("Immuno.", "Mesench.", "Prolif.", "Differen.")) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 2,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"))
umap_consOV_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensusOV), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi) +
scale_color_manual(values = clrs$consensusOV) +
guides(color = F)
umap_consOV_legend <- cowplot::get_legend(umap_consOV)
umap_mutsig <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensus_signature), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 2,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"))
umap_mutsig_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensus_signature), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = F)
umap_mutsig_legend <- cowplot::get_legend(umap_mutsig)
continuous_umap_layers <- list(
scale_color_viridis_c(),
guides(color = guide_colorbar(label.position = "right",
title.position = "top",
title.hjust = 0,
title.vjust = 1,
direction = "vertical")),
theme(legend.title = element_text(face = "bold", size = 14),
legend.key.height = unit(0.05, "npc"),
legend.key.width = unit(0.03, "npc"),
legend.position = c(-0.07, 1),
legend.justification = c("left", "top"),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 18)))
umap_umis <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = log2(nCount_RNA)), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
labs(title = "log2(#UMIs)", color = "") +
continuous_umap_layers
umap_genes <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = log2(nFeature_RNA)), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
labs(title = "log2(#Genes)", color = "") +
continuous_umap_layers
umap_mitos <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = percent.mt), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
labs(title = "MT reads [%]", color = "") +
continuous_umap_layers
umap_doublet_score <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = doublet_score), size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
labs(title = "Doublet score", color = "") +
continuous_umap_layers
total numbers
total_numbers_plot <- function(column_var) {
column_var <- enquo(column_var)
plot_data <- seu_tbl_full %>%
filter(cell_type != "Other", therapy == "pre-Rx") %>%
group_by(!!column_var) %>%
tally %>%
arrange(n) %>%
mutate(column_label = paste0(!!column_var, " (", format(n, trim=T, big.mark=","), ")")) %>%
mutate(!!column_var := ordered(!!column_var, levels = unique(!!column_var)))
common_layers <- list(
scale_y_continuous(expand = c(0, 0)),
guides(fill = F, color = F),
coord_flip(clip = "off"),
theme(axis.title = element_blank(),
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(face = "plain"))
)
p1 <- ggplot(plot_data) +
geom_point(aes(!!column_var, 0, color = !!column_var), size = 4) +
scale_color_manual(values = clrs[[as_label(column_var)]]) +
common_layers
p2 <- ggplot(plot_data) +
geom_bar(aes(!!column_var, n, fill = !!column_var), stat = "identity", width = 0.3) +
geom_text(aes(!!column_var, 1, label = column_label), hjust = 0, vjust = 0, nudge_x = 0.2) +
scale_fill_manual(values = clrs[[as_label(column_var)]]) +
common_layers
plot_grid(p1, p2, align = "v", ncol = 2, rel_widths = c(0.03, 0.97))
}
numbers_plot_cell_type <- total_numbers_plot(cell_type)
numbers_plot_tumor_supersite <- total_numbers_plot(tumor_supersite)
Mixing per cell type
pm_score_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/patient_mixing_scores.tsv") %>%
mutate(cell_type = str_replace_all(cell_type, "Monocyte", "Myeloid cell")) %>%
mutate(cell_type = str_replace_all(cell_type, "Ovarian.cancer.cell", "Ov cancer cell"))
pm_score_tbl_ordered <- pm_score_tbl %>%
filter(cell_type != "Other") %>%
group_by(cell_type) %>%
mutate(median = median(score)) %>%
ungroup() %>%
arrange(-median) %>%
mutate(cell_type = ordered(cell_type, levels = unique(cell_type)))
mixing_plot_cell_type <- ggplot(pm_score_tbl_ordered) +
geom_violin(aes(cell_type, score, fill = cell_type),
adjust = 3, color = "white", width = 1) +
geom_boxplot(aes(cell_type, score, color = cell_type),
width = 0.2, size = 1, fill = NA, outlier.size = 0.01) +
geom_boxplot(aes(cell_type, score, fill = cell_type),
width = 0.2, color = "white", outlier.shape = NA) +
geom_hline(yintercept = 0, linetype = 2, size = 0.5, color = "black") +
scale_fill_manual(values = clrs$cell_type) +
scale_color_manual(values = clrs$cell_type) +
guides(fill = F, color = F) +
scale_y_continuous(expand = c(0, 0),
limits = c(-5, 7),
breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(y = "Patient\nspecificity") +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
axis.title.x = element_blank())
# ggsave("_fig/_paper/01_patient_mixing_plot_v5.pdf", mixing_plot_cell_type,
# width = 3, height = 5)
### pairwise comparisons
pm_score_summary <- pm_score_tbl_ordered %>%
separate(cell_id, into = c("patient_id", "sample_suffix"), remove = F, extra = "drop", sep = "_") %>%
group_by(patient_id, cell_type) %>%
summarise(mean_score = mean(score))
ptt_tbl <- pairwise.t.test(pm_score_summary$mean_score, pm_score_summary$cell_type, p.adjust.method = "fdr") %$%
p.value %>%
as_tibble(rownames = "cell_type_1") %>%
gather(cell_type_2, pvalue, -cell_type_1) %>%
mutate(cell_type_2 = ordered(cell_type_2, levels = levels(pm_score_tbl_ordered$cell_type))) %>%
mutate(cell_type_1 = ordered(cell_type_1, levels = levels(pm_score_tbl_ordered$cell_type))) %>%
mutate(log10p = -log10(pvalue),
sig = pvalue < 0.1,
label = ifelse(sig, "*", "")) %>%
na.omit()
pm_comparisons <- ggplot(filter(ptt_tbl, sig)) +
# geom_tile(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), fill = log10p)) +
geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), size = log10p), color = "black", shape = 21) +
geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), fill = log10p, size = log10p), shape = 21) +
# geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), shape = sig),
# color = "white", size = 5) +
# geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), shape = sig),
# color = "black", size = 3) +
scale_fill_gradientn(colours = viridis(9)) +
scale_size(guide = F) +
# scale_color_gradientn(colours = viridis(9)) +
theme(aspect.ratio = 1,
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title = element_blank(),
plot.title = element_text(face = "plain", size = 12)) +
scale_shape_manual(values = c("", "*"), guide = F) +
# scale_x_discrete(expand = c(0, 0)) +
# scale_y_discrete(expand = c(0, 0)) +
labs(fill = "-log10(p-value)", title = "Pairwise comparisons\nof patient specificity")
consensusOV summary
consOV_tbl_summary <- seu_tbl_full %>%
select(cell_type, consensusOV, patient_id_short, tumor_supersite) %>%
mutate(consOV_cell_type = cell_type) %>%
# mutate(consOV_cell_type = ifelse(S.Score > 0.5, "Cycling cell", as.character(cell_type))) %>%
group_by(consOV_cell_type, consensusOV) %>%
tally %>%
group_by(consOV_cell_type) %>%
mutate(nrel = n/(sum(n))*100) %>%
ungroup %>%
mutate(consensusOV = ordered(consensusOV, levels = c(names(clrs$consensusOV)))) %>%
arrange(consensusOV, nrel)
heat_layers <- list(
geom_tile(),
scale_fill_gradientn(colors = magma(9)[c(-1)], limits = c(0, 100), breaks = c(0, 50, 100)),
theme(
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
legend.position = "bottom",
aspect.ratio = 4/9
),
guides(fill = guide_colorbar(title.position = "top"))
)
tcga_heat <- consOV_tbl_summary %>%
filter(consOV_cell_type != "Other") %>%
mutate(cell_type = ordered(consOV_cell_type, levels = unique(c("Myeloid cell", "Fibroblast", "Cycling cell", names(clrs$cell_type))))) %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
ggplot(aes(cell_type, consensusOV, fill = nrel)) +
labs(fill = "Fraction\nof cells [%]") +
heat_layers
tcga_heat_wide <- consOV_tbl_summary %>%
filter(consOV_cell_type != "Other") %>%
mutate(cell_type = ordered(consOV_cell_type, levels = unique(c("Myeloid cell", "Fibroblast", "Cycling cell", names(clrs$cell_type))))) %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
ggplot(aes(consensusOV, cell_type, fill = nrel)) +
labs(fill = "Fraction\nof cells [%]") +
heat_layers +
theme(aspect.ratio = 9/4, legend.position = "right")
# tcga_heat + guides(fill = F)
tcga_heat_legend <- get_legend(tcga_heat)
Figure 2 upper panel
arrow <- arrow(angle = 20, type = "closed", length = unit(0.1, "npc"))
umap_coord_anno <- ggplot(tibble(group = c("UMAP1", "UMAP2"),
x = c(0, 0), xend = c(1, 0),
y = c(0, 0), yend = c(0, 1),
lx = c(0.5, -0.15), ly = c(-0.15, 0.5),
angle = c(0, 90))) +
geom_segment(aes(x, y, xend = xend, yend = yend, group = group),
arrow = arrow, size = 1, lineend = "round") +
geom_text(aes(lx, ly, label = group, angle = angle), size = 4) +
theme_void() +
coord_fixed(xlim = c(-0.3, 1), ylim = c(-0.3, 1))
add_umap_coord <- function(gg_obj) {
p <- ggdraw() +
draw_plot(gg_obj, x = 0, y = 0, width = 1, height = 1) +
draw_plot(umap_coord_anno, x = -0.015, y = -0.02, width = 0.2, height = 0.2)
return(p)
}
ggdraw() +
## left
draw_label("Patient", x = 0.25, y = 0.98, size = 20) +
draw_plot(add_umap_coord(umap_patient), x = 0, y = 0, width = 0.5, height = 1) +
draw_label(paste0("n = ", nrow(seu_tbl_full)),
x = 0.25, y = 0.03, size = 16, hjust = 0) +
## right upper
draw_plot(mixing_plot_cell_type, x = 0.5+0.5*0, y = 0.66, width = 0.5*0.3, height = 0.33) +
draw_label("Cell type", x = 0.71, y = 0.98, size = 20, hjust = 0) +
draw_plot(umap_cell_type_mini, x = 0.83, y = 0.68, width = 0.5*1/3, height = 0.31) +
draw_plot(numbers_plot_cell_type, x = 0.7, y = 0.651, width = 0.13, height = 0.315) +
## right middle
draw_label("TCGA", x = 0.53+0.5*0, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_consOV_mini, x = 0.5+0.5*0, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Signature", x = 0.53+0.5*0.33, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_mutsig_mini, x = 0.5+0.5*0.33, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Site", x = 0.53+0.5*0.66, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_site_mini, x = 0.5+0.5*0.66, y = 0.3, width = 0.5*1/3, height = 0.31) +
## right lower
draw_grob(umap_consOV_legend, x = 0.52+0.5*0, y = -0.67, hjust = 0, vjust = 0) +
draw_plot(tcga_heat + guides(fill = F), x = 0.49+0.5*0, y = -0.03, width = 0.18, height = 0.3) +
draw_grob(tcga_heat_legend, x = 0.68, y = -0.35) +
draw_grob(umap_mutsig_legend, x = 0.52+0.5*0.33, y = -0.67, hjust = 0, vjust = 0) +
draw_grob(umap_site_legend, x = 0.52+0.5*0.66, y = -0.67, hjust = 0, vjust = 0)
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v6.png", width = 20, height = 10)
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v6.pdf", width = 20, height = 10)
ggdraw() +
## left
draw_label("Patient", x = 0.25, y = 0.98, size = 20) +
draw_plot(add_umap_coord(umap_patient), x = 0, y = 0, width = 0.5, height = 1) +
draw_label(paste0("n = ", nrow(seu_tbl_full)),
x = 0.25, y = 0.03, size = 16, hjust = 0) +
## right upper
draw_plot(mixing_plot_cell_type, x = 0.5+0.5*0, y = 0.66, width = 0.5*0.3, height = 0.33) +
draw_label("Cell type", x = 0.725, y = 0.98, size = 20, hjust = 0) +
draw_plot(umap_cell_type_mini, x = 0.83, y = 0.68, width = 0.5*1/3, height = 0.31) +
draw_plot(numbers_plot_cell_type, x = 0.715, y = 0.651, width = 0.13, height = 0.315) +
## right middle
draw_label("TCGA", x = 0.53+0.5*0, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_consOV_mini, x = 0.5+0.5*0, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Site", x = 0.725, y = 0.6, size = 20, hjust = 0) +
draw_plot(numbers_plot_tumor_supersite, x = 0.715, y = 0.34, width = 0.13, height = 0.25) +
draw_plot(umap_site_mini, x = 0.5+0.5*0.66, y = 0.3, width = 0.5*1/3, height = 0.31) +
## right lower
draw_plot(tcga_heat + guides(fill = F), x = 0.49+0.5*0, y = -0.03, width = 0.18, height = 0.3) +
draw_grob(tcga_heat_legend, x = 0.68, y = -0.35) +
draw_grob(umap_consOV_legend, x = 0.52+0.5*0, y = -0.67, hjust = 0, vjust = 0)
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v6.png", width = 20, height = 10)
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v6.pdf", width = 20, height = 10)
S1
## QC histograms
qc_tbl <- seu_tbl_full %>%
mutate(`log2(#UMIs)` = log2(nCount_RNA),
`log2(#Genes)` = log2(nFeature_RNA),
`log2(#UMIs/#Genes)` = `log2(#UMIs)`-`log2(#Genes)`,
`MT reads [%]` = percent.mt) %>%
select(`log2(#UMIs)`,
`log2(#Genes)`,
# `log2(#UMIs/#Genes)`,
`MT reads [%]`, cell_type) %>%
gather(key, value, -cell_type) %>%
group_by(key) %>%
mutate(median_value = median(value))
qc_tbl <- bind_rows(qc_tbl, mutate(qc_tbl, cell_type = "All cell types")) %>%
mutate(cell_type = ordered(cell_type,
levels = rev(c("All cell types", names(clrs$cell_type)))))
qc_plot <- ggplot(qc_tbl) +
geom_violin(aes(cell_type, value, fill = cell_type),
color = "white", width = 1) +
geom_boxplot(aes(cell_type, value, color = cell_type),
width = 0.15, size = 1, fill = NA, outlier.shape = NA) +
geom_boxplot(aes(cell_type, value), fill = NA,
width = 0.15, color = "white", outlier.shape = NA) +
geom_hline(aes(yintercept = median_value),
data = distinct(qc_tbl, key, median_value)) +
facet_wrap(~key, scales = "free_x", strip.position = "bottom") +
scale_fill_manual(values = c(clrs$cell_type, `All cell types` = "grey10"),
guide = F) +
scale_color_manual(values = c(clrs$cell_type, `All cell types` = "grey10"),
guide = F) +
coord_flip() +
theme(axis.title = element_blank(),
strip.placement = "outside")
custom_guide <- guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1, label.position = "right")
mutsig_legend <- cowplot::get_legend(umap_mutsig + theme(legend.title=element_text()) + labs(color = "Mutational\nsignature") + guides(color = custom_guide))
consOV_legend <- cowplot::get_legend(umap_consOV + theme(legend.title=element_text()) + labs(color = "TCGA subtype") + guides(color = custom_guide))
cell_type_legend <- cowplot::get_legend(umap_cell_type + theme(legend.title=element_text(), legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0), color = "black")) + labs(color = "Cell type") + guides(color = custom_guide))
site_legend <- cowplot::get_legend(umap_site + theme(legend.title=element_text()) + labs(color = "Site") + guides(color = custom_guide))
figS1_panel <- ggdraw() +
# draw_plot(add_umap_coord(umap_mutsig + guides(color = F) + labs(title = "Mutational signature")), width = 0.24, height = 0.48, x = 0.01, y = 0.5) +
draw_plot(add_umap_coord(umap_umis), width = 0.24, height = 0.48, x = 0.01, y = 0.5) +
draw_plot(add_umap_coord(umap_genes), width = 0.24, height = 0.48, x = 0.25, y = 0.5) +
draw_plot(add_umap_coord(umap_mitos), width = 0.24, height = 0.48, x = 0.01, y = 0) +
draw_plot(qc_plot, width = 0.5, height = 0.48, x = 0.5, y = 0.5) +
draw_grob(mutsig_legend, x = 0.55, y = 0.48, hjust = 0, vjust = 1) +
draw_grob(consOV_legend, x = 0.55, y = 0.25, hjust = 0, vjust = 1) +
draw_grob(cell_type_legend, x = 0.7, y = 0.48, hjust = 0, vjust = 1) +
draw_grob(site_legend, x = 0.85, y = 0.48, hjust = 0, vjust = 1) +
draw_label("A", x = 0.01, y = 0.98, fontface = "bold", size = 22) +
draw_label("B", x = 0.52, y = 0.98, fontface = "bold", size = 22) +
draw_label("C", x = 0.52, y = 0.48, fontface = "bold", size = 22)
figS1_panel
ggsave("_fig/002_cohort/002_supplementary_umap.pdf", figS1_panel, width = 16, height = 9)
# xf <- 1.33
#
# ggdraw() +
# draw_plot(add_umap_coord(umap_site), x = 0, y = 0, width = 0.5/xf, height = 1) +
# draw_plot(umap_patient_mini, x = 0.5/xf, y = 0.5, width = 0.25/xf, height = 0.5) +
# draw_plot(umap_cell_type_mini, x = 0.5/xf, y = 0, width = 0.25/xf, height = 0.5) +
# draw_plot(umap_mutsig_mini, x = 0.94/xf, y = 0.5, width = 0.25/xf, height = 0.5) +
# draw_plot(umap_consOV_mini, x = 0.94/xf, y = 0, width = 0.25/xf, height = 0.5) +
# draw_grob(umap_cell_type_legend, x = 0.56, y = -0.53, hjust = 0, vjust = 0) +
# draw_plot(total_numbers_cell_type, x = 0.565, y = 0.005, width = 0.13, height = 0.45) +
# draw_grob(umap_patient_legend, x = 0.56, y = -0.05, hjust = 0, vjust = 0) +
# draw_grob(umap_mutsig_legend, x = 0.9, y = -0.05, hjust = 0, vjust = 0) +
# draw_grob(umap_consOV_legend, x = 0.9, y = -0.55, hjust = 0, vjust = 0) +
# draw_label("Site", x = 0.26/xf, y = 0.98, size = 20) +
# draw_label("Patient", x = 0.52/xf, y = 0.98, size = 20, hjust = 0) +
# draw_label("Cell type", x = 0.52/xf, y = 0.49, size = 20, hjust = 0) +
# draw_label("Signature", x = 0.95/xf, y = 0.98, size = 20, hjust = 0) +
# draw_label("TCGA", x = 0.95/xf, y = 0.49, size = 20, hjust = 0)
#
# # ggsave(filename = "_fig/001x_umaps_mutsig.png", width = 16, height = 7)
# ggsave(filename = "_fig/001x_umaps_mutsig.pdf", width = 18, height = 7)